東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Battery Optimization in Microgrids U...

Jain, Prateek.

FindBook

Google Book

Amazon

博客來

Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting./
作者:	Jain, Prateek.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	95 p.
附註:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10793304
ISBN:	9780438114166

Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting.
Jain, Prateek.

Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 95 p.

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.)--Missouri University of Science and Technology, 2018.

Rising climatic concerns call for unconventional/renewable energy sources which reduce the carbon footprint. Microgrids that integrate a variety of renewable energy resources play a key role in utilizing these energy resources in a more efficient and environmentally friendly manner. Battery systems effectively help to utilize these energy resources more efficiently. This research work presents a framework based on Markov Decision Process (MDP) integrated with load and solar forecasting to derive an optimal charging/discharging action of Battery with rolling horizon implementation. The load forecasting regression models are discussed and developed. Also, various solar forecasting models like clear sky, multi-regression and Non-Linear Autoregressive Neural Network model with Exogenous time-series are discussed and compared. The control algorithm is developed to reduce the monthly billing cost by reducing the peak load demand while also maintaining the state of charge of the battery. The presented work simulates the control algorithm for one month based on historic load and solar data. The results indicate substantial cost savings are possible with the proposed algorithm.

ISBN: 9780438114166Subjects--Topical Terms:

649834
Electrical engineering.

Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting.
LDR:02170nmm a2200301 4500 001 2201066
005 20190329144318.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438114166
035 $a (MiAaPQ)AAI10793304
035 $a (MiAaPQ)missouriscitech:11216
035 $a AAI10793304
040 $a MiAaPQ $c MiAaPQ
100 1 $a Jain, Prateek. $3 3427792
245 1 0 $a Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 95 p.
500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Jonathan Kimball.
502 $a Thesis (M.S.)--Missouri University of Science and Technology, 2018.
520 $a Rising climatic concerns call for unconventional/renewable energy sources which reduce the carbon footprint. Microgrids that integrate a variety of renewable energy resources play a key role in utilizing these energy resources in a more efficient and environmentally friendly manner. Battery systems effectively help to utilize these energy resources more efficiently. This research work presents a framework based on Markov Decision Process (MDP) integrated with load and solar forecasting to derive an optimal charging/discharging action of Battery with rolling horizon implementation. The load forecasting regression models are discussed and developed. Also, various solar forecasting models like clear sky, multi-regression and Non-Linear Autoregressive Neural Network model with Exogenous time-series are discussed and compared. The control algorithm is developed to reduce the monthly billing cost by reducing the peak load demand while also maintaining the state of charge of the battery. The presented work simulates the control algorithm for one month based on historic load and solar data. The results indicate substantial cost savings are possible with the proposed algorithm.
590 $a School code: 0587.
650 4 $a Electrical engineering. $3 649834
650 4 $a Engineering. $3 586835
690 $a 0544
690 $a 0537
710 2 $a Missouri University of Science and Technology. $b Electrical Engineering. $3 2093519
773 0 $t Masters Abstracts International $g 57-06(E).
790 $a 0587
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10793304